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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Cardiovascular Syncope and Autonomic Disorders
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,208 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,208 works in the cohort · of 4,299,418page 12 of 25

Labels cover 6 of 1,208 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 1,208 of 1,208 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

afffundaboutunlabeled
Methods for validating chronometry of computerized tests
Joshua P. Salmon, Stephanie A. H. Jones, Chris P. Wright, Beverly Butler, Raymond M. Klein, Gail A. Eskes
2016· article· en· Journal of Clinical and Experimental Neuropsychology· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Key to Prevention of Bradycardia
Tumul Chowdhury, Bernhard Schaller
2016· article· en· Medicine· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Long-term recurrences and mortality in patients with noncardiac syncope
Gonzalo Barón‐Esquivias, Macarena Quintanilla, Antonio J. Díaz-Martín, Carmen Barón-Solís, C. Almeida, Carmen Selene García-Romero +6 more
2021· article· es· Revista Española de Cardiología (English Edition)· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
Prevalence of vasovagal syncope following bariatric surgery
Omar A. Al Obeed, Thamer Bin Traiki, Y. AlFahad, Maha‐Hamadien Abdulla, Mohamed N. AlAli, Abdulhamed A. Alharbi +3 more
2021· article· en· Saudi Journal of Anaesthesia· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
afffundunlabeled
Sleep syncope—A systematic review
Priya L. Raj, Robert S. Sheldon, Diane Lorenzetti, David L. Jardine, Satish R. Raj, Bert Vandenberk
2022· review· en· Frontiers in Cardiovascular Medicine· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
afffundvenueunlabeled
Role of Artificial Intelligence in Improving Syncope Management
Venkatesh Thiruganasambandamoorthy, Marc A. Probst, Timothy J. Poterucha, Roopinder K. Sandhu, Cristian Toarta, Satish R. Raj +3 more
2024· review· en· Canadian Journal of Cardiology· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
affvenueno abstractunlabeled
Geriatric Cardiology: Moving Beyond Learning by Osmosis
George Heckman, Jaspreet Bhangu, Michelle M. Graham, Sabina Keen, Deirdre E. O’Neill
2024· editorial· en· Canadian Journal of Cardiology· Medicine
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About